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The systems engineering of a network-centric distributed intelligent system of systems for robust human behavior classifications.

机译:以网络为中心的分布式智能系统的系统工程,用于健全的人类行为分类。

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摘要

Automating intelligence within sensor networks for situational awareness and responses is the overall motivational application for this dissertation. Traditionally, intelligence is manually gathered and extracted by intelligence analysts. However, there will never be enough intelligence analysts, intelligent centers, or even bandwidth (for mobile sensors) to manually extract information for intelligence from raw sensor data. Fusing a large number of sensor types and inputs is also required. All of this can be implemented and automated in an artificial intelligent (AI) hierarchy described herein, and therefore not require human power to observe, fuse, and interpret. This objective is fulfilled in this systems dissertation with several independent systems combined together to form an intelligent system of systems (SoS).;In order to design and implement an intelligent SoS, there are a number of unique contributions from this author in this dissertation. The first six listed author contributions are systems developments as Chief Engineer on the intelligent SoS and the last six contributions are novel technological developments. The following are the SoS systems developments: (1) a Fixed Camera System containing a multi-camera network (thirty-six PoE cameras) and six processing units; (2) a Kiosk System containing dual Pan/Tilt/Zoom cameras, a microphone network and two processing units; and (3) a Command and Control System containing a database on a server with dual monitors displaying an (4) interactive executive graphical user interface displaying (5) mustered personnel and (6) abnormal behavior alarms. This SoS was designed and built with novel technologies that the author developed for this SoS: (7) high-level syntactical classifiers for classifying human/object behaviors that are predefined based on sequences of (8) identified combinations of fused (9) object recognitions (e.g. body postures and face recognitions) by low-level classifiers on video data, including a (10) generalized parts-based object recognition low-level classifier. The system uses a (11) high-level syntactical classifier to recover from low-level classification errors. This intelligent SoS was built and implemented as a prototype. Additionally, preliminary transitions are underway for transitioning the prototype to a product system, such as (12) providing a Field Programmable Gate Array (FPGA) architecture for the generalized object recognition low-level classifier.
机译:传感器网络中的智能自动化以实现态势感知和响应是本文的总体动机应用。传统上,情报是由情报分析师手动收集和提取的。但是,永远不会有足够的情报分析人员,情报中心甚至带宽(用于移动传感器)来手动从原始传感器数据中提取情报信息。还需要融合大量传感器类型和输入。所有这些都可以在此处描述的人工智能(AI)层次结构中实现和自动化,因此不需要人力来观察,融合和解释。通过将几个独立的系统组合在一起构成一个智能的系统系统(SoS),可以在本系统论文中实现这一目标。为了设计和实现智能的SoS,作者在本论文中做出了许多独特的贡献。列出的前六位作者的贡献是作为智能SoS总工程师的系统开发,后六位是新颖的技术发展。 SoS系统的发展如下:(1)包含多摄像机网络(三十六个PoE摄像机)和六个处理单元的固定摄像机系统; (2)信息亭系统,其中包含双摄/俯仰/变焦摄像机,一个麦克风网络和两个处理单元; (3)一个命令和控制系统,该系统包含一个服务器上的数据库,该服务器具有双监视器,该双监视器显示(4)交互式执行图形用户界面,该界面显示(5)召集人员和(6)异常行为警报。该SoS是使用作者为此SoS开发的新颖技术设计和构建的:(7)高级句法分类器,用于基于(8)已识别的融合(9)对象识别组合的序列对人/对象行为进行预定义视频数据的低级分类器(例如,身体姿势和面部识别),包括(10)广义的基于零件的对象识别低级分类器。该系统使用(11)高级语法分类器从低级分类错误中恢复。这种智能的SoS是作为原型构建和实施的。此外,正在进行初步过渡,以将原型过渡到产品系统,例如(12),它为通用对象识别低级分类器提供了现场可编程门阵列(FPGA)体系结构。

著录项

  • 作者

    Goshorn, Deborah Ellen.;

  • 作者单位

    University of California, San Diego.;

  • 授予单位 University of California, San Diego.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 608 p.
  • 总页数 608
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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